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Observer models of perceptual development

Published online by Cambridge University Press:  10 January 2019

Marko Nardini Open the ORCID record for Marko Nardini [Opens in a new window]  and
Tessa M. Dekker
Marko Nardini
Affiliation:
Department of Psychology, Durham University, Durham DH1 3LE, United Kingdom. marko.nardini@durham.ac.ukhttp://community.dur.ac.uk/marko.nardini/
Tessa M. Dekker
Affiliation:
Department of Experimental Psychology and Institute of Ophthalmology, University College London, London WC1E 6BT, United Kingdom. t.dekker@ucl.ac.ukhttp://www.ucl.ac.uk/~ucjttb1/
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Abstract

We agree with Rahnev & Denison (R&D) that to understand perception at a process level, we must investigate why performance sometimes deviates from idealised decision models. Recent research reveals that such deviations from optimality are pervasive during perceptual development. We argue that a full understanding of perception requires a model of how perceptual systems become increasingly optimised during development.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 41 , 2018 , e238
Copyright
Copyright © Cambridge University Press 2018 

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References

Adams, R. J. & Courage, M. L. (2002) Using a single test to measure human contrast sensitivity from early childhood to maturity. Vision Research 42(9):1205–10. Available at: https://doi.org/10.1016/S0042-6989(02)00038-X.Google Scholar
Adams, W. J., Graf, E. W. & Ernst, M. O. (2004) Experience can change the “light-from-above” prior. Nature Neuroscience 7:1057–58. Available at: https://doi.org/10.1038/nn1312.Google Scholar
Blakemore, C. & Van Sluyters, R. C. (1975) Innate and environmental factors in the development of the kitten's visual cortex. Journal of Physiology 248(3):663–716. Available at: https://doi.org/10.1113/jphysiol.1975.sp010995.Google Scholar
Cangelosi, A., Schlesinger, M. & Smith, L. B. (2015) Developmental robotics: From babies to robots. MIT Press.Google Scholar
Dekker, T. M., Ban, H., van der Velde, B., Sereno, M. I., Welchman, A. E. & Nardini, M. (2015) Late development of cue integration is linked to sensory fusion in cortex. Current Biology 25(21): 2856–61. Available at: https://doi.org/10.1016/j.cub.2015.09.043.Google Scholar
Dekker, T. M. & Nardini, M. (2016) Risky visuomotor choices during rapid reaching in childhood. Developmental Science 19(3):427–39. Available at: https://doi.org/10.1111/desc.12322.Google Scholar
Gori, M., Del Viva, M., Sandini, G. & Burr, D. C. (2008) Young children do not integrate visual and haptic form information. Current Biology 18(9):694–98. Available at: https://doi.org/10.1016/j.cub.2008.04.036.Google Scholar
Hadad, B. S., Maurer, D. & Lewis, T. L. (2011) Long trajectory for the development of sensitivity to global and biological motion. Developmental Science 14:1330–39. Available at: https://doi.org/10.1111/j.1467-7687.2011.01078.x.Google Scholar
Jones, P. R. & Dekker, T. M. (2017) The development of perceptual averaging: Learning what to do, not just how to do it. Developmental Science 21:e12584. Available at: https://doi.org/10.1111/desc.12584.Google Scholar
Lawson, R. P., Mathys, C. & Rees, G. (2017) Adults with autism overestimate the volatility of the sensory environment. Nature Neuroscience 20(9):1293–99. Available at: https://doi.org/10.1038/nn.4615.Google Scholar
Manning, C., Dakin, S. C., Tibber, M. S. & Pellicano, E. (2014) Averaging, not internal noise, limits the development of coherent motion processing. Developmental Cognitive Neuroscience 10:44–56. Available at: https://doi.org/10.1016/j.dcn.2014.07.004.Google Scholar
Mondloch, C. J., Le Grand, R. & Maurer, D. (2002) Configural face processing develops more slowly than featural face processing. Perception 31:553–66. Available at: https://doi.org/10.1068/p3339.Google Scholar
Nardini, M., Bedford, R. & Mareschal, D. (2010) Fusion of visual cues is not mandatory in children. Proceedings of the National Academy of Sciences of the United States of America 107(39):17041–46. Available at: https://doi.org/10.1073/pnas.1001699107.Google Scholar
Nardini, M., Jones, P., Bedford, R. & Braddick, O. (2008) Development of cue integration in human navigation. Current Biology 18(9):689–93. Available at: https://doi.org/10.1016/j.cub.2008.04.021.Google Scholar
Ohshiro, T., Angelaki, D. E. & DeAngelis, G. C. (2011) A normalization model of multisensory integration. Nature Neuroscience 14(6):775–82. Available at: https://doi.org/10.1038/nn.2815.Google Scholar
Rosenberg, A., Patterson, J. S. & Angelaki, D. E. (2015) A computational perspective on autism. Proceedings of the National Academy of Sciences of the United States of America 112(30):9158–65. Available at: https://doi.org/10.1073/pnas.1510583112.Google Scholar
Stone, J. V. (2011) Footprints sticking out of the sand. Part 2: Children's Bayesian priors for shape and lighting direction. Perception 40(2):175–90. Available at: https://doi.org/10.1068/p6776.Google Scholar
Sweeny, T. D., Wurnitsch, N., Gopnik, A. & Whitney, D. (2015) Ensemble perception of size in 4–5-year-old children. Developmental Science 18(4):556–68. Available at: https://doi.org/10.1111/desc.12239.Google Scholar
Thomas, R., Nardini, M. & Mareschal, D. (2010) Interactions between “light-from-above” and convexity priors in visual development. Journal of Vision 10:6. Available at: https://doi.org/10.1167/10.8.6.Google Scholar
van Bergen, R. S., Ji Ma, W., Pratte, M. S. & Jehee, J. F. M. (2015) Sensory uncertainty decoded from visual cortex predicts behavior. Nature Neuroscience 18(12):1728–30. Available at: https://doi.org/10.1038/nn.4150.Google Scholar
Wang, J. X., Kurth-Nelson, Z., Tirumala, D., Soyer, H., Leibo, J. Z., Munos, R., Blundell, C., Kumaran, D. & Botivnick, M. (2017) Learning to reinforcement learn. ArXiv 1611.05763. Available at: https://arxiv.org/abs/1611.05763.Google Scholar
Wu, S.-W., Delgado, M. R. & Maloney, L. T. (2011) The neural correlates of subjective utility of monetary outcome and probability weight in economic and in motor decision under risk. Journal of Neuroscience 31(24): 8822–31. Available at: https://doi.org/10.1523/JNEUROSCI.0540-11.2011.Google Scholar